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Optical Coherence Elastography (OCE) L1-508

Medical ImagingSub-resolution displacement-tracked tissue mechanical property recovery (multi-physics joint inverse)δ=5 · advancedL_DAG = 8.4📋 Stub — not mineable
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Unclaimed Principle — open for contribution

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Forward model E

OCT signal: A(r, t) = \sqrt{R(r - u(r, t))) \cdot exp(i \cdot 2\cdot k\cdot z + i\cdot phi_0); phase difference Delta-phi(r, t1, t2) = 2\cdot k\cdot [u_z(r, t2) - u_z(r, t1)] / n_g; elasticity wave: rho \cdot d2u/dt2 = grad . \sigma + f_body; \sigma = 2 mu epsilon + \lambda tr(epsilon) I; jointly invert {Delta-phi(r, t)} \to (mu, \lambda)(r) given f_load(r, t), rho, boundary conditions

Optical Coherence Elastography (OCE): joint multi-physics forward couples (i) OCT signal acquisition where complex amplitude A(r) at depth z is recovered via low-coherence interferometry (spectral-domain or swept-source); (ii) sub-resolution displacement tracking where applied loading f_load(r, t) (compression, indentation, acoustic radiation force, or surface waves) produces displacement field u(r, t) that shifts the local scatter profile R(r - u(r, t)); (iii) tissue mechanical wave propagation governed by the linear elasticity wave equation rho * d2u/dt2 = grad . (sigma) + f_body, with constitutive law sigma = 2 mu epsilon + lambda tr(epsilon) I where epsilon is the symmetric strain tensor. The forward DAG has 8 primitives with two coupling constraints (n_c = 2): (i) Lagrangian-Eulerian displacement coupling — the deformation field u(r, t) shifts the OCT scatter profile, with phase difference Delta-phi = 2*k*u/n_g coupling phase-sensitive OCT signal to displacement; (ii) loading-to-displacement coupling — applied f_load produces u via the elasticity wave equation, with shear-wave speed c_s = sqrt(mu/rho) carrying shear-modulus information through the wave equation. Recovery is posed as the joint inverse problem that recovers (mu, lambda)(r) (or equivalently Young's modulus E(r) and Poisson ratio nu(r)) from time-resolved OCT phase data {Delta-phi(r, t)} given known applied loading f_load(r, t). Difficulty tier delta = 5 with raw condition number kappa ~ 250 (limited by OCT phase noise floor and wavelength-dependent decorrelation) and effective kappa_eff ~ 35 after physics-informed wave-equation regularization. Mismatch parameters: oct_phase_noise, scatter_decorrelation, loading_calibration_error, tissue_anisotropy, density_uncertainty, boundary_condition_uncertainty. Additive Gaussian phase noise sets the data-fidelity floor. See forward_model field for the closed-form joint imaging equation.

L-DAG

L.oct_acquisition -> L.phase_extraction -> L.displacement_tracking -> L.elasticity_wave -> L.constitutive_law -> L.applied_loading -> int.spatial -> int.temporal
L.oct_acquisitionL.phase_extractionL.displacement_trackingL.elasticity_waveL.constitutive_lawL.applied_loadingint.spatialint.temporal

Well-posedness W

Existence:
true
Uniqueness:
conditional
Stability:
conditional
κ:
250

Existence of recovered Lame parameter maps (mu, lambda)(r) is guaranteed within the declared Omega bounds. Uniqueness holds for shear-wave-mode loading at multiple frequencies (2D-3D shear-wave dispersion analysis); compression-mode and surface-wave OCE are conditionally unique requiring boundary-condition specification and density assumption. Stability is moderately conditioned (kappa_eff ~ 35 after physics-informed wave-equation regularization) — oct_phase_noise dominates displacement-tracking precision; tissue_anisotropy contributes off-diagonal Lame-tensor bias; density_uncertainty contributes a scaling factor of order rho^(-1/2). Joint Hadamard well-posedness for the coupled OCT-elasticity forward is established by Schmitt 1998 (foundational), Wang-Kirkpatrick-Hinds 2007 (phase-sensitive OCE), Kennedy-Wijesinghe-Sampson 2014 (compression OCE), Larin-Sampson 2017 (review), Kirby et al. 2017 (shear-wave OCE methods), and Wijesinghe et al. 2019 (computational OCE).

Solvability C

Solver class:
linear-operator + convex optimisation [physics-informed displacement-to-stiffness inversion; Helmholtz-equation regularized shear-wave inversion; finite-element-method forward + iterative gradient descent] | nonlinear-least-squares [Levenberg-Marquardt for parametric stiffness models] | linear-operator + deep neural prior [OCE-Net, ElastoNet]
Convergence rate q:
2
Complexity:
O(H * W * Z * N_frames * (axial_resolution_um)^(-1)) per iteration via FFT-based phase extraction + FEM-based wave-equation forward; learned variants O(H W Z N_frames * F_theta_cost) per forward pass

Specs (0)

No L2 specs registered yet for this principle.